Showing posts with label variable. Show all posts
Showing posts with label variable. Show all posts

Thursday, January 20, 2022

Factor Analysis Principal Components Analysis

 


Factor analysis (FA) is a statistical method of reducing a large set of data to a smaller set by identifying patterns in the data that have common characteristics. Factor analysis is sometimes called data reduction or dimension reduction.

The original numerical values in the data set are observed variables (also called manifest variables) such as the items in a large survey or test. Factor analysis may find patterns characterized by a shared statistical relationship representing a factor, which is also called a dimension. A researcher examines the content of the items linked to this factor and chooses a factor label such as verbal skills for related items on an intelligence test.

The factors may be treated as variables in additional research. These are secondary variables. Because they are created from the observed variables, they are considered latent variables. For example, if 5 items on a personality test are associated with one factor labeled "agreeableness" then agreeableness is a latent variable.

The set of identified factors is referred to as the structure of the data set. If the data are from a test then researchers refer to the structure of the test.

Factors are identified based on the variance they account for in the data. The amount of variance explained by a factor is represented by an eigenvalue. Researchers look for eigenvalues of 1.0 or more to consider a factor to be a valuable contribution to explaining the underlying structure of a data set.

Not all factors are equal. That is, when more than one factor have been identified, they will contribute differently to explaining the variance in the data set.


Different kinds of Factor Analysis

Exploratory Factor Analysis (EFA). When researchers do not know the structure of a data set, they use EFA to discover the set of factors.

Confirmatory Factor Analysis (CFA).  When researchers wish to test a hypothesis about a data set, they perform CFA. For example, if they believe their forgiveness questionnaire contains one factor called forgiveness, they can examine the structure to see if one factor best accounts for the data set. If one factor is the best solution then they have found support for their hypothesis.

Principal Components Analysis (PCA) is a common form of confirmatory factor analysis. 

Factor Analysis is important to understanding tests in Counseling and Psychotherapy. See

Applied Statistics Concepts for Counselors on   AMAZON or   GOOGLE








Factor Analysis is often used to reduce the data collected from survey research. 

Learn More in Creating Surveys on AMAZON or GOOGLE








Please check out my website   www.suttong.com

   and see my books on   AMAZON       or  GOOGLE STORE

Also, consider connecting with me on    FACEBOOK   Geoff W. Sutton    

   TWITTER  @Geoff.W.Sutton    

You can read many published articles at no charge:

  Academia   Geoff W Sutton     ResearchGate   Geoffrey W Sutton 







Friday, May 7, 2021

Continuous variables in behavioral research

 

Continuous variable. A variable having a wide range of numerical values, such as intelligence, achievement, and personality variables.

Example: Scores on a Big Five test of personality are often reported as T-Scores for each of the five scales. Most people obtain scores in the range of 40 to 60 but it is possible to obtain lower and higher scores. The point of the example is that the scores are continuous and cover a wide range. 

Researchers can group people based on their scores using groups labels like "high" and "low" perhaps by deciding that the median would be the score to separate high and low scores. Changing the continuous variable results in the formation of a grouping variable or categorical variable.

Example 2: Age is a continuous variable beginning at birth and continuing to death. Researchers can group people by age and create a grouping or categorical value.

Learn More about variables in Creating Surveys on AMAZON or GOOGLE



Please check out my website   www.suttong.com

   and see my books on   AMAZON       or  GOOGLE STORE

Also, consider connecting with me on    FACEBOOK   Geoff W. Sutton    

   TWITTER  @Geoff.W.Sutton    

You can read many published articles at no charge:

  Academia   Geoff W Sutton     ResearchGate   Geoffrey W Sutton 


Confounding variables in behavioral research

 

A Confounding variable is a variable that produces unexpected changes in the dependent variable and therefore interferes with interpreting the capacity of an independent variable to produce or explain changes in a dependent variable.

Example: During a study of anxiety that includes measures of anxiety and stress, some participants watch a documentary about the treatment of anxiety and some do not. Documentary-watching may confound the results if watching the program influenced the scores on the measures of anxiety and stress. Similarly, some participants may be exposed to a source of stress in their environment but others are not, which could interfere with interpreting the results.


Learn More about research methods and variables in Creating Surveys on AMAZON or GOOGLE



Please check out my website   www.suttong.com

   and see my books on   AMAZON       or  GOOGLE STORE

Also, consider connecting with me on    FACEBOOK   Geoff W. Sutton    

   TWITTER  @Geoff.W.Sutton    

You can read many published articles at no charge:

  Academia   Geoff W Sutton     ResearchGate   Geoffrey W Sutton 


Categorical or Grouping variable in Behavioral Research

 

Categorical variables are those variables having two or more groups or levels such as sex, ethnicity, and religious group. 

They may be called independent variables even though they are not true independent variables under experimental control.

Categorical variables, also called grouping variables, can be created from continuous variables. For example, researchers often obtain the age of their study participants. Age is a continuous variable but sometimes, researchers group ages together and compare how people of different age groups answer questions on a survey.


Learn More in Creating Surveys on AMAZON or GOOGLE


Please check out my website   www.suttong.com

   and see my books on   AMAZON       or  GOOGLE STORE

Also, consider connecting with me on    FACEBOOK   Geoff W. Sutton    

   TWITTER  @Geoff.W.Sutton    

You can read many published articles at no charge:

  Academia   Geoff W Sutton     ResearchGate   Geoffrey W Sutton 


Thursday, January 14, 2021

Independent Variable IV

 


Independent variable (IV). The variable in a research study that a researcher manipulates to determine if another variable, the dependent variable, changes when the IV changes.

Creating Surveys on AMAZON    or   GOOGLE  Worldwide







Links to Connections

Checkout My Website   www.suttong.com

  

See my Books

  AMAZON       

 

  GOOGLE STORE

 

FOLLOW me on

   FACEBOOK   Geoff W. Sutton  

  

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Read published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton 


Dependent Variable DV

 


Dependent variable (DV). The variable in a research study that is expected to change when a researcher varies the level of an independent variable.

Example: In a counseling study designed to help people forgive, forgiveness would be the DV and the survey used to measure forgiveness would be the Dependent Measure.

Creating Surveys on AMAZON    or   GOOGLE  Worldwide









Links to Connections

 

Checkout My Website   www.suttong.com

  

See my Books

 

  AMAZON       

 

  GOOGLE STORE

 

JOIN me on

 

   FACEBOOK   Geoff W. Sutton  

  

   TWITTER  @Geoff.W.Sutton

 

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Read many published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton 


Tuesday, January 5, 2021

ANCOVA in Counseling & Behavioral Research

 


ANCOVA

ANCOVA is a procedure like ANOVA except researchers can study the effects of one or more independent variables on a dependent variable after adjusting for other variables, called covariates, which were not a primary focus of the study. The letter C in ANCOVA stands for covariate. There can be several covariates in a study. In testing for differences among groups experiencing different leadership styles, we could study the effects on employee satisfaction after adjusting for a covariate of years of employment. A key word in ANCOVA studies is adjusting. Analysts adjust the scores based on information about the covariate before testing for significant differences.


Basic features of an ANCOVA:


Independent or grouping Variable = 1 or more

Dependent or criterion Variable = 1

Covariates = 1 or more


An test indicates significance overall and for specific effects or relationships.

A commonly reported measure of effect size is eta squared.

value reveals the probability of a significant relationship-- one that is not due to chance factors.

Read more about ANCOVA in the following books.


Applied Statistics Concepts for Counselors on AMAZON or GOOGLE






Creating Surveys on AMAZON    or   GOOGLE  Worldwide










Links to Connections

Checkout My Website   www.suttong.com

  

See my Books

  AMAZON       

 

  GOOGLE STORE

 

FOLLOW me on

   FACEBOOK   Geoff W. Sutton  

  

   TWITTER  @Geoff.W.Sutton

 

   PINTEREST  www.pinterest.com/GeoffWSutton

 

Read published articles:

 

  Academia   Geoff W Sutton   

 

  ResearchGate   Geoffrey W Sutton 


Chi-Square

 

Chi-Square is a statistical test that can be used to analyze results from categorical variables. Categorical variables are variables that contain clearly different groups. The chi-square statistic is used with frequency data. 


The chi-square value is reported with a probability (p) value indicating significance. 


For example, we can use chi-square to test for an association between frequency of attendance at organizational meetings and age groups (category variable). 


Common measures of effect size associated with chi-square analyses are Cramer’s V or the phi coefficient.


Read more about Chi Square and other statistics in the following books.



Applied Statistics: Concepts for Counselors on AMAZON or GOOGLE









Creating Surveys on AMAZON    or   GOOGLE  Worldwide










Links to Connections

 

Please check out my website   www.suttong.com

   and see my books on   AMAZON       or  GOOGLE STORE

Also, consider connecting with me on    FACEBOOK   Geoff W. Sutton    

   TWITTER  @Geoff.W.Sutton    

You can read many published articles at no charge:

  Academia   Geoff W Sutton     ResearchGate   Geoffrey W Sutton 


Regression Data Analysis

 

Regression is a statistical procedure used to predict values on a criterion variable from the knowledge of values obtained on a predictor variable. For example, an organization may use an employment screening test or survey that has been useful in the past to predict how well employees perform a particular type of job. The criterion variable is a continuous variable, meaning it can have a range of score values. Predictor variables may be either continuous or categorical variables. When there is only one predictor variable and one criterion variable, the procedure is known as simple regression.

 

Read more about regression in these books.


Applied Statistics on AMAZON or GOOGLE











Creating Surveys on AMAZON or GOOGLE












Checkout My Page   www.suttong.com

  

My Books  AMAZON       and           GOOGLE STORE

 

FOLLOW me on

   FACEBOOK   Geoff W. Sutton  

  

   TWITTER  @Geoff.W.Sutton

 

   PINTEREST  www.pinterest.com/GeoffWSutton

 

Articles:

   Academia   Geoff W Sutton   

 

   ResearchGate   Geoffrey W Sutton 


Assessment of Spirituality and Religious Sentiments (ASPIRES) scale- Short Form

  Scale  name: Assessment of Spirituality and Religious Sentiments (ASPIRES) scale- Short Form Scale overview : The Assessment of Spirit...